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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation
Prompt Engineering & Structured Output	20%	- Structured data generation 1. Output validation and reliability 2. JSON schema enforcement - Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow
Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution - Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP server and client setup 2. Tools, resources, and prompts integration - Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q31-Q36):

NEW QUESTION # 31
Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

A. Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
B. Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.
C. Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.
D. Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.

Answer: D

Explanation:
Structured error output gives the agent enough information to respond usefully. Including the error type, whether the issue is recoverable, and actionable guidance allows the agent to distinguish malformed tracking IDs, missing shipments, and temporary API failures, then suggest the correct next step to the user.

NEW QUESTION # 32
A developer at quicktechie.com is building a highly experimental MCP server testing a new vector database. They want to use it locally in Claude Code without exposing the configuration to the rest of the team. Which configuration file should they use?

A. .claude/rules/mcp.md
B. .mcp.json at the project root
C. mcp_local.config
D. ~/.claude.json

Answer: D

Explanation:
Personal, experimental, or user-specific MCP configurations should be placed in the user-scoped -/.claude.json file, preventing them from being shared with the team via the project repository's .mcp.json.

NEW QUESTION # 33
When using Claude Code for continuous integration (CI) and multi-pass code review workflows, which of the following practices are highly recommended? Choose 2 correct answers.

A. Have the same session that generated the code review its own changes to ensure maximum context preservation.
B. Use the Message Batches API to process blocking pre-merge pull request checks.
C. Isolate the code generator session from the code reviewer session to eliminate confirmation bias.
D. Use the -p flag to ensure non-interactive execution so the pipeline does not hang.

Answer: C,D

Explanation:
For CI/CD workflows, the -p (or --print) flag is strictly required to run non-interactively. Furthermore, using independent review instances (isolating the generator from the reviewer) removes the original reasoning context and avoids confirmation bias. Message Batches are not suited for blocking pre-merge checks due to latency limits.

NEW QUESTION # 34
In a multi-agent orchestration, the synthesis agent occasionally misuses a web search tool instead of synthesizing the provided findings. How should you prevent this cross-role misuse according to the principle of scoped tool access?

A. Use the fork session command to isolate the web search tool execution.
B. Implement a PostToolUse hook to silently drop web search results when executed by the synthesis agent.
C. Add a capitalized warning in the system prompt instructing the synthesis agent to ignore web search capabilities.
D. Remove the web search tool from the synthesis agent's allowed tool set entirely.

Answer: D

Explanation:
Agents with tools outside their specialization tend to misuse them. The most robust architectural fix for cross-role tool misuse is scoped tool access: explicitly removing the irrelevant tool from that specific subagent's allowed array so it strictly focuses on its assigned role.

NEW QUESTION # 35
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?

A. Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
B. Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.
C. Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context.
D. Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.

Answer: C

Explanation:
Structuring synthesis around established versus contested findings preserves uncertainty without collapsing conflicting evidence into a false single answer. It allows the report to retain source- specific context, such as analyst methodology variability and peer-reviewed confidence intervals, while still presenting a clear and useful interpretation.

NEW QUESTION # 36
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